A review on speech emotion recognition using deep learning and attention mechanism
Emotions are an integral part of human interactions and are significant factors in determining
user satisfaction or customer opinion. speech emotion recognition (SER) modules also play …
user satisfaction or customer opinion. speech emotion recognition (SER) modules also play …
Modeling Speech Emotion Recognition via Attention-Oriented Parallel CNN Encoders
Meticulous learning of human emotions through speech is an indispensable function of
modern speech emotion recognition (SER) models. Consequently, deriving and interpreting …
modern speech emotion recognition (SER) models. Consequently, deriving and interpreting …
Predicting expressive speaking style from text in end-to-end speech synthesis
Global Style Tokens (GSTs) are a recently-proposed method to learn latent disentangled
representations of high-dimensional data. GSTs can be used within Tacotron, a state-of-the …
representations of high-dimensional data. GSTs can be used within Tacotron, a state-of-the …
Transformer encoder with multi-modal multi-head attention for continuous affect recognition
Continuous affect recognition is becoming an increasingly attractive research topic in
affective computing. Previous works mainly focused on modelling the temporal dependency …
affective computing. Previous works mainly focused on modelling the temporal dependency …
Improving cross-corpus speech emotion recognition with adversarial discriminative domain generalization (ADDoG)
J Gideon, MG McInnis… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Automatic speech emotion recognition provides computers with critical context to enable
user understanding. While methods trained and tested within the same dataset have been …
user understanding. While methods trained and tested within the same dataset have been …
Few-shot learning for fine-grained emotion recognition using physiological signals
Fine-grained emotion recognition can model the temporal dynamics of emotions, which is
more precise than predicting one emotion retrospectively for an activity (eg, video clip …
more precise than predicting one emotion retrospectively for an activity (eg, video clip …
Learning speech emotion representations in the quaternion domain
The modeling of human emotion expression in speech signals is an important, yet
challenging task. The high resource demand of speech emotion recognition models …
challenging task. The high resource demand of speech emotion recognition models …
The priori emotion dataset: Linking mood to emotion detected in-the-wild
Bipolar Disorder is a chronic psychiatric illness characterized by pathological mood swings
associated with severe disruptions in emotion regulation. Clinical monitoring of mood is key …
associated with severe disruptions in emotion regulation. Clinical monitoring of mood is key …
[PDF][PDF] Convolutional Neural Network Based Speaker De-Identification.
Concealing speaker identity in speech signals refers to the task of speaker de-identification,
which helps protect the privacy of a speaker. Although, both linguistic and paralinguistic …
which helps protect the privacy of a speaker. Although, both linguistic and paralinguistic …
Continuous emotion recognition in videos by fusing facial expression, head pose and eye gaze
Continuous emotion recognition is of great significance in affective computing and human-
computer interaction. Most of existing methods for video based continuous emotion …
computer interaction. Most of existing methods for video based continuous emotion …